Hybrid AI - The Best of Both Worlds

A Perfect Blend of Expert-Based Knowledge and Data-Driven Machine Learning

With new payment platforms, digitalization initiatives, and other innovative fintech innovations sprouting up across the financial services industry at a faster rate than ever, data pools are growing like never before. In parallel, increased digital and online transaction volumes have offered fraudsters a new avenue for consumer exploitation.

Therefore, more and more financial institutions see a growing demand for a flexible system that can handle the challenge of mass data analysis within milliseconds and state-of-the-art financial crime prevention.

Introducing Hybrid AI ...

... an approach that combines the computational capabilities of AI with human intuition and expertise, creating one powerful financial crime fighting strategy.

RiskShield's Hybrid AI approach blends knowledge-based expert scenarios with Machine Learning (ML) techniques, resulting in a more comprehensive and practical risk and fraud solution. This advanced approach unites the benefits of both methods in one powerful system, allowing for the strengths of each to be fully realized.

 

Knowledge-based Expert Systems 

  • Knowledge-based expert systems excel in fast and efficient data processing.
  • Scenarios in these systems are transparent and easily understandable.
  • Updates to scenarios can be swiftly implemented to adapt to changes or new fraud types.
  • They effectively reduce false positives in fraud detection.
  • However, fraud patterns change faster than they can be translated into decision models.
  • There is a high requirement for highly skilled experts who can design complex scenarios
Man solving a wooden puzzle (GJKD Banks)
Woman looking at screens full of data

Machine Learning

  • ML algorithms swiftly and accurately analyze large data sets to detect fraudulent or risky patterns.
  • They enhance the accuracy of fraud detection and risk analysis, aiding in the identification of emerging threats.
  • ML algorithms can reduce false positives by recognizing legitimate patterns, saving time and resources.
  • Automation of fraud detection tasks by ML algorithms improves efficiency and reduces costs.
  • However, they rely on ample high-quality data for effective training and may face limitations due to data availability and quality.

How does it work?

Can we describe on proper level how Hybrid AI actually works? Is this where give some details aobut the parallel and sequential approach?

How can we do this in another way, other than text?
Video? Graphics? Animated slides?

 

Your benefit from Hybrid AI - use cases

Hybrid Decision-Making

Integrating precise rules-based logic with the adaptive capabilities of machine learning enhances decision-making, leading to improved accuracy, comprehensive analysis, and scalability. This powerful combination unites the guiding logic of human-defined rules with nuanced insights from large data sets, allowing business analysts to focus on strategic tasks while ensuring robust fraud protection and achieving more effective and sophisticated decision-making.

Explainable AI

By using tree-based models, rule writers gain interpretable decision-making based on data. These models highlight key attributes and conditions in RiskShield Case Management, helping rule writers develop new rules and detect emerging fraud patterns. The "ML Powered by INFORM" page offers detailed machine learning outputs, including scores, predicted classes, hints, and important variables, providing deeper insights into the decision-making process.

Alert Prioritization

Hybrid AI ranks alerted transactions by risk, prioritizing higher-risk alerts for faster review and response. This leads to quicker triage and more efficient fraud case handling. The machine learning model learns from past decisions, improving its accuracy in scoring and prioritizing alerts. This reduces false positives, enabling investigators to focus on critical alerts while maintaining regulatory compliance.

Curious to find out more?

Then please do not hesitate to contact us. Just fill in your details below. We will get in touch with you shortly. 

Andrea Vieten

Andrea Vieten

Product Management - Risk & Fraud

Andrea Vieten is product manager for INFORM’s RiskShield solution, bringing over 15 years of expertise and knowledge in risk and fraud. With a history of various positions, she has guided numerous financial institutions to implement intelligent decision systems for risk, fraud, processes and compliance. Combining her technical and business knowledge with experience in sales and marketing, she continues to develop customer-oriented strategies to meet the evolving needs of the financial industry.

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Hybrid AI